In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
In [2]:
import pandas as pd

df = pd.read_csv("weather.csv")
df.head()
Out[2]:
MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir WindGustSpeed WindDir9am WindDir3pm WindSpeed9am ... Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainToday RISK_MM RainTomorrow
0 8.0 24.3 0.0 3.4 6.3 NW 30.0 SW NW 6.0 ... 29 1019.7 1015.0 7 7 14.4 23.6 No 3.6 Yes
1 14.0 26.9 3.6 4.4 9.7 ENE 39.0 E W 4.0 ... 36 1012.4 1008.4 5 3 17.5 25.7 Yes 3.6 Yes
2 13.7 23.4 3.6 5.8 3.3 NW 85.0 N NNE 6.0 ... 69 1009.5 1007.2 8 7 15.4 20.2 Yes 39.8 Yes
3 13.3 15.5 39.8 7.2 9.1 NW 54.0 WNW W 30.0 ... 56 1005.5 1007.0 2 7 13.5 14.1 Yes 2.8 Yes
4 7.6 16.1 2.8 5.6 10.6 SSE 50.0 SSE ESE 20.0 ... 49 1018.3 1018.5 7 7 11.1 15.4 Yes 0.0 No

5 rows × 22 columns

In [3]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 366 entries, 0 to 365
Data columns (total 22 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   MinTemp        366 non-null    float64
 1   MaxTemp        366 non-null    float64
 2   Rainfall       366 non-null    float64
 3   Evaporation    366 non-null    float64
 4   Sunshine       363 non-null    float64
 5   WindGustDir    363 non-null    object 
 6   WindGustSpeed  364 non-null    float64
 7   WindDir9am     335 non-null    object 
 8   WindDir3pm     365 non-null    object 
 9   WindSpeed9am   359 non-null    float64
 10  WindSpeed3pm   366 non-null    int64  
 11  Humidity9am    366 non-null    int64  
 12  Humidity3pm    366 non-null    int64  
 13  Pressure9am    366 non-null    float64
 14  Pressure3pm    366 non-null    float64
 15  Cloud9am       366 non-null    int64  
 16  Cloud3pm       366 non-null    int64  
 17  Temp9am        366 non-null    float64
 18  Temp3pm        366 non-null    float64
 19  RainToday      366 non-null    object 
 20  RISK_MM        366 non-null    float64
 21  RainTomorrow   366 non-null    object 
dtypes: float64(12), int64(5), object(5)
memory usage: 63.0+ KB
In [4]:
df.describe()
Out[4]:
MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustSpeed WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RISK_MM
count 366.000000 366.000000 366.000000 366.000000 363.000000 364.000000 359.000000 366.000000 366.000000 366.000000 366.000000 366.000000 366.000000 366.000000 366.000000 366.000000 366.000000
mean 7.265574 20.550273 1.428415 4.521858 7.909366 39.840659 9.651811 17.986339 72.035519 44.519126 1019.709016 1016.810383 3.890710 4.024590 12.358470 19.230874 1.428415
std 6.025800 6.690516 4.225800 2.669383 3.481517 13.059807 7.951929 8.856997 13.137058 16.850947 6.686212 6.469422 2.956131 2.666268 5.630832 6.640346 4.225800
min -5.300000 7.600000 0.000000 0.200000 0.000000 13.000000 0.000000 0.000000 36.000000 13.000000 996.500000 996.800000 0.000000 0.000000 0.100000 5.100000 0.000000
25% 2.300000 15.025000 0.000000 2.200000 5.950000 31.000000 6.000000 11.000000 64.000000 32.250000 1015.350000 1012.800000 1.000000 1.000000 7.625000 14.150000 0.000000
50% 7.450000 19.650000 0.000000 4.200000 8.600000 39.000000 7.000000 17.000000 72.000000 43.000000 1020.150000 1017.400000 3.500000 4.000000 12.550000 18.550000 0.000000
75% 12.500000 25.500000 0.200000 6.400000 10.500000 46.000000 13.000000 24.000000 81.000000 55.000000 1024.475000 1021.475000 7.000000 7.000000 17.000000 24.000000 0.200000
max 20.900000 35.800000 39.800000 13.800000 13.600000 98.000000 41.000000 52.000000 99.000000 96.000000 1035.700000 1033.200000 8.000000 8.000000 24.700000 34.500000 39.800000
In [5]:
df.shape
Out[5]:
(366, 22)
In [6]:
df.columns
Out[6]:
Index(['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine',
       'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',
       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',
       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',
       'Temp3pm', 'RainToday', 'RISK_MM', 'RainTomorrow'],
      dtype='object')
In [7]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.pairplot(df)
plt.show()
No description has been provided for this image
In [8]:
plt.figure(figsize=(8,5))
plt.scatter(df['MaxTemp'], df['Humidity3pm'])
plt.xlabel('Max Temperature')
plt.ylabel('Humidity at 3pm')
plt.title('Max Temperature vs Humidity (3pm)')
plt.show()
No description has been provided for this image
In [9]:
sns.pairplot(df[['MinTemp', 'MaxTemp', 'Rainfall']])
plt.show()
No description has been provided for this image
In [10]:
plt.figure(figsize=(8,5))
plt.scatter(df['MinTemp'], df['Humidity9am'])
plt.xlabel('Min Temperature')
plt.ylabel('Humidity at 9am')
plt.title('Min Temperature vs Humidity (9am)')
plt.show()
No description has been provided for this image
In [11]:
numeric_df = df.select_dtypes(include='number')

plt.figure(figsize=(12,8))
sns.heatmap(numeric_df.corr(), annot=False, cmap="coolwarm")
plt.title("Correlation Heatmap of Weather Data")
plt.show()
No description has been provided for this image
In [12]:
# Select only numeric columns
numeric_df = df.select_dtypes(include='number')

# Separate features and target
X = numeric_df.drop('Rainfall', axis=1)
y = numeric_df['Rainfall']

X.shape, y.shape
Out[12]:
((366, 16), (366,))
In [13]:
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

X_train.shape, X_test.shape
Out[13]:
((292, 16), (74, 16))
In [15]:
numeric_df.isnull().sum()
Out[15]:
MinTemp          0
MaxTemp          0
Rainfall         0
Evaporation      0
Sunshine         3
WindGustSpeed    2
WindSpeed9am     7
WindSpeed3pm     0
Humidity9am      0
Humidity3pm      0
Pressure9am      0
Pressure3pm      0
Cloud9am         0
Cloud3pm         0
Temp9am          0
Temp3pm          0
RISK_MM          0
dtype: int64
In [16]:
import numpy as np
np.isinf(numeric_df).sum()
Out[16]:
MinTemp          0
MaxTemp          0
Rainfall         0
Evaporation      0
Sunshine         0
WindGustSpeed    0
WindSpeed9am     0
WindSpeed3pm     0
Humidity9am      0
Humidity3pm      0
Pressure9am      0
Pressure3pm      0
Cloud9am         0
Cloud3pm         0
Temp9am          0
Temp3pm          0
RISK_MM          0
dtype: int64
In [17]:
from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy='mean')

X_train_imputed = imputer.fit_transform(X_train)
X_test_imputed = imputer.transform(X_test)
In [18]:
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error

model = LinearRegression()
model.fit(X_train_imputed, y_train)

y_pred = model.predict(X_test_imputed)

r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)

r2, mse
Out[18]:
(0.19041298786157834, 34.2256782862109)
In [21]:
X = df[['MinTemp', 'MaxTemp']]
y = df['Rainfall']
In [22]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [24]:
model = LinearRegression()
model.fit(X_train, y_train)
Out[24]:
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
fit_intercept  True
copy_X  True
tol  1e-06
n_jobs  None
positive  False
In [25]:
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error for Rainfall Prediction: {mse}')

Mean Squared Error for Rainfall Prediction: 37.0768456005826
In [27]:
df.groupby("RainToday")["MaxTemp"].mean()
Out[27]:
RainToday
No     20.756667
Yes    19.612121
Name: MaxTemp, dtype: float64
In [28]:
df.groupby("RainTomorrow")["MaxTemp"].mean()
Out[28]:
RainTomorrow
No     20.396000
Yes    21.251515
Name: MaxTemp, dtype: float64